Research on privacy protection method based on deep reinforcement learning algorithm in data mining

被引：0

作者：

Cai, Yan ^{[1
]}

Xue, Rui ^{[1
]}

机构：

[1] School of Computer and Artificial Intelligence, Henan Finance University, Henan, Zhengzhou,450046, China

来源：

International Journal of Computational Systems Engineering | 2024年 / 8卷 / 3-4期

关键词：

Protecting data privacy is a critical issue in information security. However, traditional methods often hinder data mining efficiency and accuracy. This study aims to balance data security and mining efficiency to improve accuracy while ensuring privacy. Using the deep reinforcement learning algorithm, the target-optimised deep Q-network (T-DQN) is proposed. Multiple standard network datasets are used for testing. The proposed algorithm achieves higher Bayesian network representation accuracy (7.2%–24.7%) compared to PrivBayes under different datasets. Revenue is also increased (12%–17% higher than Sarsa and 29%–32% higher than Q-learning). Weak regret value is lower (698–2,573 lower than Sarsa and 984–1,327 lower than Q-learning). The algorithm demonstrates good convergence, adaptability, and superior performance compared to other algorithms. It provides a reference for improving privacy protection efficiency in data mining. Copyright © 2024 Inderscience Enterprises Ltd;

D O I：

10.1504/IJCSYSE.2024.142764

中图分类号：

学科分类号：

摘要：

引用

页码：210 / 219

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